Instatus → DuckDB
AI-first ETL from Instatus into DuckDB. Governed entities, incremental sync, typed landing tables.
How Datrise loads Instatus into DuckDB
Datrise syncs Instatus's records, events, and configuration objects into DuckDB as a typed table per source entity in a DuckDB file. Flexible or custom fields land in JSON or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP WITH TIME ZONE.
Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.
Ideal for local and notebook analytics without standing up a server.
Endpoints
Instatus: SaaS or API data source for analytics and warehouse sync.
DuckDB: In-process analytics database for fast local OLAP.
How Instatus entities map to DuckDB
| Instatus entity | DuckDB object | Notes |
|---|---|---|
| records | instatus_records | id PK · custom fields → JSON or STRUCT columns |
| events | instatus_events | TIMESTAMP WITH TIME ZONE events |
| configuration objects | instatus_configuration_objects | id PK · linked to instatus_records |
FAQ
How does Datrise handle Instatus's custom fields in DuckDB?
Flexible values are stored as JSON or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native DuckDB types.
How does the Instatus to DuckDB sync stay up to date?
It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.
Related pipelines
More destinations for Instatus
Early access
Connect Instatus to DuckDB the easy way
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